edit

Author: MorvanZhou

Reinforcement_learning_TUT/11_Dyna_Q/RL_brain.py

@@ -52,27 +52,28 @@ def check_state_exist(self, state):
 
 
 class EnvModel:
-    """Similar to the memory buffer of DQN, you can store past experiences in here"""
+    """Similar to the memory buffer in DQN, you can store past experiences in here.
+    Alternatively, the model can generate next state and reward signal accurately."""
     def __init__(self, actions):
         # the simplest case is to think about the model is a memory which has all past transition information
         self.actions = actions
-        self.memory = pd.DataFrame(columns=actions, dtype=np.object)
+        self.database = pd.DataFrame(columns=actions, dtype=np.object)
 
     def store_transition(self, s, a, r, s_):
-        if s not in self.memory.index:
-            self.memory = self.memory.append(
+        if s not in self.database.index:
+            self.database = self.database.append(
                 pd.Series(
                     [None] * len(self.actions),
-                    index=self.memory.columns,
+                    index=self.database.columns,
                     name=s,
                 ))
-        self.memory.set_value(s, a, (r, s_))
+        self.database.set_value(s, a, (r, s_))
 
     def sample_s_a(self):
-        s = np.random.choice(self.memory.index)
-        a = np.random.choice(self.memory.ix[s].dropna().index)    # filter out the None value
+        s = np.random.choice(self.database.index)
+        a = np.random.choice(self.database.ix[s].dropna().index)    # filter out the None value
         return s, a
 
     def get_r_s_(self, s, a):
-        r, s_ = self.memory.ix[s, a]
+        r, s_ = self.database.ix[s, a]
         return r, s_

Reinforcement_learning_TUT/README.md

@@ -1,6 +1,9 @@
-# Reinforcement Methods and Tutorials
+# Reinforcement Learning Methods and Tutorials
+
+In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
 
 All methods mentioned below have their video and text tutorial in Chinese. Visit [莫烦 Python](https://morvanzhou.github.io/tutorials/) for more.
+If you speak Chinese, you can watch my [Youtube channel](https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg) as well.
 
 
 * [Simple entry example](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/1_command_line_reinforcement_learning)